[
disp_out.reshape(-1, 1),
dataset_img['t_disps'], #t_disps[ntest],
dataset_img['gtruths'], # gtruths[ntest],
dbg_cost_nw.reshape(-1, 1),
dbg_cost_w.reshape(-1, 1),
dbg_d.reshape(-1, 1),
dbg_avg_disparity.reshape(-1, 1),
dbg_gt_disparity.reshape(-1, 1),
dbg_offs.reshape(-1, 1)
],
1)
np.save(result_file, rslt.reshape(HEIGHT, WIDTH, -1))
rslt = qsf.eval_results(result_file,
ABSOLUTE_DISPARITY,
radius=CLUSTER_RADIUS,
logfile=lf)
img_gain_test0 = rslt[0][0] / rslt[0][1]
img_gain_test9 = rslt[9][0] / rslt[9][1]
if SAVE_TIFFS:
qsf.result_npy_to_tiff(result_file,
ABSOLUTE_DISPARITY,
fix_nan=True,
labels=SLICE_LABELS,
logfile=lf)
"""
Remove dataset_img (if it is not [0] to reduce memory footprint
"""
if ntest > 0:
image_data[ntest] = None
if lf:
qsf.print_time("Running inferred model, stage2", end=" ")
disp_out, = sess.run([stage2_out_sparse],
feed_dict={ph_ntile_out: img_ntile })
qsf.print_time("Done.")
result_file = files['result'][nimg].replace('.npy','-infer.npy') #not to overwrite training result files that are more complete
try:
os.makedirs(os.path.dirname(result_file))
except:
pass
rslt = np.concatenate(
[disp_out.reshape(-1,1),
dataset_img['t_disps'], #t_disps[ntest],
dataset_img['gtruths'], # gtruths[ntest],
],1)
np.save(result_file, rslt.reshape(HEIGHT,WIDTH,-1))
rslt = qsf.eval_results(result_file, ABSOLUTE_DISPARITY, radius=CLUSTER_RADIUS, logfile=lf) # (re-loads results). Only uses first 4 layers
if SAVE_TIFFS:
qsf.result_npy_to_tiff(result_file, ABSOLUTE_DISPARITY, fix_nan = True,labels=SLICE_LABELS, logfile=lf)
"""
Remove dataset_img (if it is not [0] to reduce memory footprint
"""
image_data[nimg] = None
"""
Save MetaGraph to Saved_Model in *.pb (protocol buffer) format to
be able to use from Java
"""
# force clean
shutil.rmtree(dirs['exportdir'], ignore_errors=True)
builder = tf.saved_model.builder.SavedModelBuilder(dirs['exportdir'])
builder.add_meta_graph_and_variables(sess,[tf.saved_model.tag_constants.SERVING],main_op=tf.local_variables_initializer())